Participant selects political party affiliation which then becomes the political party of the majority group.
Majority group opinions deviate away from participant’s choice on 8 issues with possible condition randomly selected (deviation threshold = [0/25/50/75/100%]).
Method change: deviance is tied to specific agents (e.g. the deviant agents are the same from trial to trial)
8 issues, 8 agents
| 0 (N=68) |
0.25 (N=62) |
0.5 (N=43) |
0.75 (N=63) |
1 (N=51) |
Overall (N=287) |
|
|---|---|---|---|---|---|---|
| age | ||||||
| Mean (SD) | 36.2 (13.1) | 39.0 (14.9) | 36.7 (12.0) | 35.1 (11.2) | 36.2 (14.2) | 36.6 (13.1) |
| Median [Min, Max] | 34.0 [19.0, 67.0] | 36.5 [18.0, 74.0] | 35.0 [19.0, 68.0] | 32.0 [19.0, 65.0] | 31.0 [19.0, 72.0] | 33.0 [18.0, 74.0] |
| race | ||||||
| American Indian or Alaska Native | 1 (1.5%) | 0 (0%) | 1 (2.3%) | 2 (3.2%) | 0 (0%) | 4 (1.4%) |
| Asian | 5 (7.4%) | 8 (12.9%) | 4 (9.3%) | 5 (7.9%) | 5 (9.8%) | 27 (9.4%) |
| Black or African-American | 10 (14.7%) | 6 (9.7%) | 2 (4.7%) | 9 (14.3%) | 6 (11.8%) | 33 (11.5%) |
| Hispanic/Latinx | 8 (11.8%) | 3 (4.8%) | 0 (0%) | 3 (4.8%) | 4 (7.8%) | 18 (6.3%) |
| Other | 1 (1.5%) | 1 (1.6%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.7%) |
| White | 43 (63.2%) | 43 (69.4%) | 35 (81.4%) | 44 (69.8%) | 36 (70.6%) | 201 (70.0%) |
| Native Hawaiian or Other Pacific Islander | 0 (0%) | 1 (1.6%) | 1 (2.3%) | 0 (0%) | 0 (0%) | 2 (0.7%) |
| gender | ||||||
| Man | 26 (38.2%) | 24 (38.7%) | 14 (32.6%) | 33 (52.4%) | 24 (47.1%) | 121 (42.2%) |
| Woman | 42 (61.8%) | 34 (54.8%) | 27 (62.8%) | 26 (41.3%) | 26 (51.0%) | 155 (54.0%) |
| Non-binary | 0 (0%) | 4 (6.5%) | 2 (4.7%) | 4 (6.3%) | 1 (2.0%) | 11 (3.8%) |
| polparty | ||||||
| Democratic | 34 (50.0%) | 33 (53.2%) | 20 (46.5%) | 34 (54.0%) | 30 (58.8%) | 151 (52.6%) |
| Independent | 25 (36.8%) | 25 (40.3%) | 17 (39.5%) | 19 (30.2%) | 12 (23.5%) | 98 (34.1%) |
| Republican | 9 (13.2%) | 4 (6.5%) | 6 (14.0%) | 10 (15.9%) | 9 (17.6%) | 38 (13.2%) |
| 0 (N=2) |
0.5 (N=3) |
0.75 (N=2) |
1 (N=4) |
Overall (N=11) |
|
|---|---|---|---|---|---|
| age | |||||
| Mean (SD) | 38.5 (2.12) | 32.3 (13.5) | 31.0 (9.90) | 36.5 (11.6) | 34.7 (9.79) |
| Median [Min, Max] | 38.5 [37.0, 40.0] | 32.0 [19.0, 46.0] | 31.0 [24.0, 38.0] | 40.0 [20.0, 46.0] | 37.0 [19.0, 46.0] |
| race | |||||
| Black or African-American | 1 (50.0%) | 0 (0%) | 0 (0%) | 1 (25.0%) | 2 (18.2%) |
| White | 1 (50.0%) | 3 (100%) | 1 (50.0%) | 3 (75.0%) | 8 (72.7%) |
| Hispanic/Latinx | 0 (0%) | 0 (0%) | 1 (50.0%) | 0 (0%) | 1 (9.1%) |
| gender | |||||
| Man | 1 (50.0%) | 1 (33.3%) | 2 (100%) | 3 (75.0%) | 7 (63.6%) |
| Woman | 1 (50.0%) | 2 (66.7%) | 0 (0%) | 1 (25.0%) | 4 (36.4%) |
| polparty | |||||
| Democratic | 1 (50.0%) | 1 (33.3%) | 1 (50.0%) | 2 (50.0%) | 5 (45.5%) |
| Republican | 1 (50.0%) | 1 (33.3%) | 0 (0%) | 1 (25.0%) | 3 (27.3%) |
| Independent | 0 (0%) | 1 (33.3%) | 1 (50.0%) | 1 (25.0%) | 3 (27.3%) |
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: corrresp
Chisq Df Pr(>Chisq)
opinion_round 249.450 1 < 2.2e-16 ***
Deviant_threshold 173.987 4 < 2.2e-16 ***
opinion_round:Deviant_threshold 32.911 4 1.245e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1 opinion_round.trend SE df asymp.LCL asymp.UCL z.ratio p.value
overall 0.253 0.0155 Inf 0.222 0.283 16.340 <.0001
Results are averaged over the levels of: Deviant_threshold
Confidence level used: 0.95 $emmeans
Deviant_threshold emmean SE df asymp.LCL asymp.UCL z.ratio p.value
0 2.029 0.114 Inf 1.804 2.253 17.721 <.0001
0.25 0.973 0.114 Inf 0.749 1.197 8.520 <.0001
0.5 0.648 0.136 Inf 0.381 0.914 4.762 <.0001
0.75 0.890 0.115 Inf 0.665 1.114 7.766 <.0001
1 2.258 0.138 Inf 1.988 2.529 16.378 <.0001
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df asymp.LCL
Deviant_threshold0 - Deviant_threshold0.25 1.0555 0.160 Inf 0.618
Deviant_threshold0 - Deviant_threshold0.5 1.3811 0.177 Inf 0.899
Deviant_threshold0 - Deviant_threshold0.75 1.1391 0.161 Inf 0.701
Deviant_threshold0 - Deviant_threshold1 -0.2294 0.177 Inf -0.714
Deviant_threshold0.25 - Deviant_threshold0.5 0.3256 0.177 Inf -0.156
Deviant_threshold0.25 - Deviant_threshold0.75 0.0837 0.161 Inf -0.354
Deviant_threshold0.25 - Deviant_threshold1 -1.2849 0.178 Inf -1.770
Deviant_threshold0.5 - Deviant_threshold0.75 -0.2420 0.177 Inf -0.724
Deviant_threshold0.5 - Deviant_threshold1 -1.6105 0.193 Inf -2.136
Deviant_threshold0.75 - Deviant_threshold1 -1.3686 0.178 Inf -1.853
asymp.UCL z.ratio p.value
1.493 6.577 <.0001
1.863 7.816 <.0001
1.577 7.097 <.0001
0.255 -1.293 0.6957
0.807 1.844 0.3483
0.522 0.521 0.9853
-0.800 -7.232 <.0001
0.240 -1.369 0.6476
-1.085 -8.366 <.0001
-0.884 -7.709 <.0001
Results are given on the log odds ratio (not the response) scale.
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 5 estimates
P value adjustment: tukey method for comparing a family of 5 estimates $emtrends
Deviant_threshold opinion_round.trend SE df asymp.LCL asymp.UCL z.ratio
0 0.203 0.0319 Inf 0.141 0.266 6.382
0.25 0.165 0.0301 Inf 0.106 0.224 5.485
0.5 0.193 0.0356 Inf 0.124 0.263 5.427
0.75 0.275 0.0307 Inf 0.215 0.335 8.950
1 0.426 0.0396 Inf 0.348 0.503 10.748
p.value
<.0001
<.0001
<.0001
<.0001
<.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df asymp.LCL
Deviant_threshold0 - Deviant_threshold0.25 0.03818 0.0436 Inf -0.0809
Deviant_threshold0 - Deviant_threshold0.5 0.00991 0.0477 Inf -0.1201
Deviant_threshold0 - Deviant_threshold0.75 -0.07146 0.0439 Inf -0.1913
Deviant_threshold0 - Deviant_threshold1 -0.22238 0.0503 Inf -0.3597
Deviant_threshold0.25 - Deviant_threshold0.5 -0.02827 0.0466 Inf -0.1553
Deviant_threshold0.25 - Deviant_threshold0.75 -0.10965 0.0428 Inf -0.2264
Deviant_threshold0.25 - Deviant_threshold1 -0.26056 0.0494 Inf -0.3954
Deviant_threshold0.5 - Deviant_threshold0.75 -0.08137 0.0469 Inf -0.2093
Deviant_threshold0.5 - Deviant_threshold1 -0.23229 0.0530 Inf -0.3770
Deviant_threshold0.75 - Deviant_threshold1 -0.15092 0.0496 Inf -0.2862
asymp.UCL z.ratio p.value
0.15722 0.875 0.9062
0.13992 0.208 0.9996
0.04838 -1.627 0.4803
-0.08505 -4.417 0.0001
0.09873 -0.607 0.9740
0.00707 -2.563 0.0774
-0.12571 -5.271 <.0001
0.04650 -1.736 0.4118
-0.08761 -4.380 0.0001
-0.01561 -3.042 0.0199
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 5 estimates
P value adjustment: tukey method for comparing a family of 5 estimates Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
targetpair 43689 43689 1 351.70 169.9655 < 2.2e-16 ***
Deviant_threshold 2471 2471 1 244.39 9.6138 0.002158 **
targetpair:Deviant_threshold 2006 2006 1 329.31 7.8057 0.005513 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1$emtrends
targetpair Deviant_threshold.trend SE df lower.CL upper.CL t.ratio p.value
Mismatch -3.94 3.57 334 -10.97 3.09 -1.102 0.2711
Match 12.52 2.90 282 6.81 18.23 4.315 <.0001
Degrees-of-freedom method: satterthwaite
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio p.value
Mismatch - Match -16.5 5.89 329 -28 -4.87 -2.794 0.0055
Degrees-of-freedom method: satterthwaite
Confidence level used: 0.95 Analysis of Variance Table
Response: k
Df Sum Sq Mean Sq F value Pr(>F)
Deviant_threshold 4 54.25 13.5617 9.1748 5.558e-07 ***
Residuals 282 416.84 1.4782
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1$emmeans
Deviant_threshold emmean SE df lower.CL upper.CL t.ratio p.value
0 1.59 0.147 282 1.30 1.88 10.761 <.0001
0.25 2.58 0.154 282 2.28 2.89 16.719 <.0001
0.5 2.52 0.185 282 2.16 2.89 13.619 <.0001
0.75 2.69 0.153 282 2.39 2.99 17.578 <.0001
1 2.58 0.170 282 2.24 2.91 15.133 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL
Deviant_threshold0 - Deviant_threshold0.25 -0.99496 0.213 282 -1.581
Deviant_threshold0 - Deviant_threshold0.5 -0.93848 0.237 282 -1.589
Deviant_threshold0 - Deviant_threshold0.75 -1.10597 0.213 282 -1.690
Deviant_threshold0 - Deviant_threshold1 -0.98987 0.225 282 -1.608
Deviant_threshold0.25 - Deviant_threshold0.5 0.05648 0.241 282 -0.606
Deviant_threshold0.25 - Deviant_threshold0.75 -0.11101 0.217 282 -0.708
Deviant_threshold0.25 - Deviant_threshold1 0.00508 0.230 282 -0.626
Deviant_threshold0.5 - Deviant_threshold0.75 -0.16749 0.240 282 -0.828
Deviant_threshold0.5 - Deviant_threshold1 -0.05139 0.252 282 -0.742
Deviant_threshold0.75 - Deviant_threshold1 0.11610 0.229 282 -0.513
upper.CL t.ratio p.value
-0.409 -4.660 <.0001
-0.288 -3.962 0.0009
-0.522 -5.202 <.0001
-0.372 -4.395 0.0002
0.719 0.234 0.9993
0.486 -0.510 0.9863
0.636 0.022 1.0000
0.493 -0.696 0.9571
0.640 -0.204 0.9996
0.745 0.507 0.9866
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 5 estimates
P value adjustment: tukey method for comparing a family of 5 estimates Deviant_threshold emmean SE df null t.ratio p.value
0 1.59 0.147 282 2 -2.805 0.0027
0.25 2.58 0.154 282 2 3.766 0.9999
0.5 2.52 0.185 282 2 2.832 0.9975
0.75 2.69 0.153 282 2 4.521 1.0000
1 2.58 0.170 282 2 3.386 0.9996
P values are left-tailed # A tibble: 2 Ă— 13
model term estimate std.error statistic p.value conf.low
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 below_.5 Deviant_threshold -38.9 9.80 -3.97 0.000106 -58.2
2 above_.5 Deviant_threshold 58.3 10.3 5.64 0.0000000767 37.9
conf.high r.squared adj.r.squared df df.residual nobs
<dbl> <dbl> <dbl> <dbl> <int> <int>
1 -19.6 0.0844 0.0791 1 171 173
2 78.7 0.171 0.165 1 155 157Analysis of Variance Table
Response: confidence
Df Sum Sq Mean Sq F value Pr(>F)
deviance 4 34701 8675.3 14.674 6.674e-11 ***
Residuals 282 166721 591.2
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1$emmeans
deviance emmean SE df lower.CL upper.CL t.ratio p.value
0 57.2 2.95 282 51.4 63.0 19.386 <.0001
0.25 40.7 3.09 282 34.7 46.8 13.194 <.0001
0.5 38.9 3.71 282 31.6 46.2 10.499 <.0001
0.75 40.4 3.06 282 34.3 46.4 13.177 <.0001
1 67.2 3.40 282 60.5 73.9 19.736 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio
deviance0 - deviance0.25 16.420 4.27 282 4.70 28.14 3.846
deviance0 - deviance0.5 18.232 4.74 282 5.22 31.24 3.848
deviance0 - deviance0.75 16.797 4.25 282 5.12 28.47 3.950
deviance0 - deviance1 -10.034 4.50 282 -22.40 2.33 -2.228
deviance0.25 - deviance0.5 1.812 4.83 282 -11.44 15.06 0.375
deviance0.25 - deviance0.75 0.377 4.35 282 -11.57 12.32 0.087
deviance0.25 - deviance1 -26.454 4.60 282 -39.07 -13.83 -5.755
deviance0.5 - deviance0.75 -1.435 4.81 282 -14.64 11.77 -0.298
deviance0.5 - deviance1 -28.266 5.03 282 -42.09 -14.44 -5.615
deviance0.75 - deviance1 -26.831 4.58 282 -39.41 -14.26 -5.858
p.value
0.0014
0.0014
0.0009
0.1725
0.9958
1.0000
<.0001
0.9983
<.0001
<.0001
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 5 estimates
P value adjustment: tukey method for comparing a family of 5 estimates | 0 (N=68) |
0.25 (N=62) |
0.5 (N=43) |
0.75 (N=63) |
1 (N=51) |
Overall (N=287) |
|
|---|---|---|---|---|---|---|
| pred_par | ||||||
| Yes | 54 (79.4%) | 40 (64.5%) | 23 (53.5%) | 26 (41.3%) | 4 (7.8%) | 147 (51.2%) |
| No | 14 (20.6%) | 22 (35.5%) | 20 (46.5%) | 37 (58.7%) | 47 (92.2%) | 140 (48.8%) |
# A tibble: 4 Ă— 14
# Groups: pred_par [2]
pred_par id term estimate std.error statistic p.value
<lgl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 FALSE below_.5 Deviant_threshold 6.53 16.9 0.385 0.701
2 FALSE above_.5 Deviant_threshold 72.3 12.8 5.67 0.000000136
3 TRUE below_.5 Deviant_threshold -50.8 11.7 -4.36 0.0000288
4 TRUE above_.5 Deviant_threshold 9.82 21.8 0.450 0.655
conf.low conf.high r.squared adj.r.squared df df.residual nobs
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 -27.4 40.5 0.00274 -0.0157 1 54 56
2 47.0 97.6 0.239 0.232 1 102 104
3 -73.9 -27.7 0.142 0.134 1 115 117
4 -34.0 53.6 0.00395 -0.0156 1 51 53Analysis of Variance Table
Response: confidence
Df Sum Sq Mean Sq F value Pr(>F)
deviance 4 34701 8675.3 15.5658 1.683e-11 ***
pred_par 1 2954 2953.5 5.2994 0.022075 *
deviance:pred_par 4 9386 2346.4 4.2101 0.002519 **
Residuals 277 154381 557.3
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis of Variance Table
Response: groupid
Df Sum Sq Mean Sq F value Pr(>F)
Deviant_threshold 1 117339 117339 269.28 < 2.2e-16 ***
Residuals 285 124187 436
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1 Deviant_threshold.trend SE df lower.CL upper.CL t.ratio p.value
overall -56.1 3.42 285 -62.8 -49.3 -16.410 <.0001
Confidence level used: 0.95 Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Deviant_threshold 125.10 31.27 4 287 0.3531 0.841798
time 693.60 693.60 1 287 7.8307 0.005484 **
Deviant_threshold:time 209.02 52.26 4 287 0.5900 0.670163
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1$emmeans
time emmean SE df lower.CL upper.CL t.ratio p.value
before 68.5 1.49 331 65.5 71.4 45.943 <.0001
after 66.2 1.49 331 63.3 69.2 44.447 <.0001
Results are averaged over the levels of: Deviant_threshold
Degrees-of-freedom method: satterthwaite
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio p.value
before - after 2.23 0.797 287 0.661 3.8 2.798 0.0055
Results are averaged over the levels of: Deviant_threshold
Degrees-of-freedom method: satterthwaite
Confidence level used: 0.95 For the similarity ratings:
Does it make sense to break into deviant/nondeviant learning given design?
Last opinion analysis, how to operationalize given the design.